Quality of life measures in pediatric multiple sclerosis: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To identify generic measures used to measure quality of life (QoL) in pediatric multiple sclerosis research, estimate an overall score of children and adolescents with pediatric multiple sclerosis, and compare the scores to scores of typically developing children and adolescents. METHOD: A systematic search was conducted on four databases. All studies were included if: the sample was children with pediatric demyelinating disorders; self-reported QoL/health-related quality of life (HRQoL) measures or results were reported; and the mean age of the sample was below 21 years. Quality of the included articles was appraised using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist and the Mixed Methods Appraisal Tool checklist. A meta-analysis was also conducted. RESULTS: A total of 12 full-text articles were included. Content analysis showed that many components of QoL were not included in the measures. Seven studies were included in the meta-analysis. The meta-analyzed score was 75.7 (95% confidence interval 71.2-80.3) with a pooled standard deviation of 16.6. Scores of typically developing children and children with pediatric multiple sclerosis were similar. INTERPRETATION: Most measures assessed HRQoL and not QoL. Development of a condition-specific measure of QoL for children and adolescents with pediatric multiple sclerosis would make an important contribution to the field. What this paper adds Health-related quality of life (HRQoL) measures were used to measure quality of life in pediatric multiple sclerosis. HRQoL scores in pediatric multiple sclerosis were similar to typically developing children and adolescents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".